Abstract
ObjectivesQyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists.MethodsDice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland–Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists.ResultsThe lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes.ConclusionsQyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.Key Points• QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists.• QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods.• QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.
Highlights
Neurological disorders represent a major public health problem in Europe and the rest of the world [1]
The present study aims to describe the comparison between the performance of the brain segmentation algorithms included in QyScore® and the manual segmentations or manual segmentation correction conducted by three expert neuroradiologists
Each database was constituted of healthy controls (HCs) and clinical patients as follows: grey matter (GM) and white matter (WM) database (24 HC, 2 Alzheimer’s disease (AD), 2 multiple sclerosis (MS), 2 Parkinson’s disease (PD)), HP and AM database
Summary
Neurological disorders represent a major public health problem in Europe and the rest of the world [1]. Automated MRI segmentation methods have been used in addition to visual analysis and manual segmentation assessments [4,5,6], improving the early diagnosis of neurological disease and the development of effective drugs [7,8,9]. Largescale multi-institutional research studies [10, 11] have worked in synergy for the implementation of standardized imaging acquisition protocols in the research environment and clinical setting [12, 13]. These advancements highlight a need for an MRI volumetric analysis tool suitable for routine clinical use. Few automated segmentation software are currently approved by regulatory agencies (such as the FDA) and included in the clinical routine workflow
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